Bayesian stopping rules for improvement of local minima algorithms in global optimization

Autor:	J. Abaffy, Marco Gaviano, A Dolci
Rok vydání:	1994
Předmět:	Maxima and minima Mathematical optimization Sequence Control and Optimization Applied Mathematics Bayesian probability Stopping rules Optimal stopping Function (mathematics) Management Science and Operations Research Algorithm Global optimization Mathematics
Zdroj:	Optimization. 30:215-226
ISSN:	1029-4945 0233-1934
DOI:	10.1080/02331939408843985
Popis:	The authors are concerned with algorithms which identify the global minimum of a function by finding a sequence of local minima of decreasing function values. Two different approaches, both based on a bayesian analysis, are considered. These allow to propose various stopping rules; specifically the most significant of them depends on an estimate of the probability that the last found minimum is global.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3f6c4be2ed00e03e9292bc6e40c4f2ce https://doi.org/10.1080/02331939408843985 Zobrazit plný text záznamu